The State Radio Monitoring Center Testing Center

Beijing, China

The State Radio Monitoring Center Testing Center

Beijing, China

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Wang P.,Beijing University of Posts and Telecommunications | Li R.,The State Radio Monitoring Center Testing Center | Zhao C.,Beijing University of Posts and Telecommunications | Liu X.,Beijing University of Posts and Telecommunications | Zhang Y.,Beijing University of Posts and Telecommunications
IET Communications | Year: 2016

This study addresses the problem of interference mitigation in multi-cell time division duplex cellular networks with large-scale antennas. Network performance in such systems is hampered by pilot contamination effect, which results in the inter-cell interference. To mitigate the interference, the authors propose a space-time pilot design-based approach to jointly eliminate interference from both time domain and space domain. The authors' space-time pilot design divides a cellular network into different groups which proceed channel estimation alternately in time. Moreover, a low-rate coordination assisted pilot assignment scheme is added into each cell group during its pilot phase. This pilot assignment scheme relies on the minimum mean square of the updated Bayesian channel estimation. It can make use of the second-order statistical information about the users' channel vectors within the same group to assign an identical pilot sequence to users that tend to interfere with each other at a lower level. On the basis of space-time pilot design, the base station can proceed the maximal ratio transmitting and maximal ratio combining. With rigorous theoretic proof, their joint approach is able to significantly decrease interference. Simulations verified its excellent performance promotion. © 2016, The Institution of Engineering and Technology.


Kai S.,Beijing University of Posts and Telecommunications | He W.,The State Radio monitoring center Testing Center | Han L.,Beijing University of Posts and Telecommunications
Journal of Next Generation Information Technology | Year: 2013

As P2P architecture has many advantages over centralized one, P2P application gets more and more widely used nowadays. However, operators still use centralized architecture in their network, which brings many problems. To promote operators' network, we propose a P2P based architecture to construct the core network and also compare our architecture with that of Skype and discuss their differences in feature, explain in detail the reason why our architecture better meets the requirements of the operators. Being more light-weighted than the mainstream P2P architecture, the new network architecture could build a direct connection between two voice terminals communicating with each other, hence allowing both signaling messages and media messages travel directly. By utilizing reload protocol in the P2P overlay, the proposed method successfully breaks the limit of using a third party machine to relay for signaling messages in the mainstream P2P network. As a result, we can greatly lower the cost of operators' investment when they build the telecommunication network architecture in practice, which brings considerable benefits to operators. The study shows a possibility for future's architecture for operators' network.


Shen J.-L.,Tongji University | Ding Q.-F.,The State Radio monitoring center Testing Center
International Journal of Simulation: Systems, Science and Technology | Year: 2016

In this paper, we prompt a new method of personalization recommendation of mobile business based on the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalization recommendation algorithm based on rough set is proposed The algorithm refine the user ratings data usin, dimensionality reduction, then uses a new similarity measurf to find the target users’ neighbors, and then generate! recommendations. To prove our algorithm's effectiveness, the authors conduct experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective. © 2016, UK Simulation Society. All rights reserved.

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